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Data-Driven Chance-Constrained Regulation Capacity Offering for Distributed Energy Resources
Hongcai Zhang1; Zechun Hu1; Eric Munsing2; Scott J. Moura2,3; Yonghua Song1,4
2018-02-23
Source PublicationIEEE Transactions on Smart Grid
ISSN1949-3053
Volume10Issue:3Pages:2713-2725
Abstract

This paper studies the behavior of a strategic aggregator offering regulation capacity on behalf of a group of distributed energy resources (DERs, e.g., plug-in electric vehicles) in a power market. Our objective is to maximize the aggregator's revenue while controlling the risk of penalties due to poor service delivery. To achieve this goal, we propose data-driven risk-averse strategies to effectively handle uncertainties in: 1) the DER parameters (e.g., load demands and flexibilities) and 2) sub-hourly regulation signals (to the accuracy of every few seconds). We design both the day-ahead and the hour-ahead strategies. In the day-ahead model, we develop a two-stage stochastic program to roughly model the above uncertainties, which achieves computational efficiency by leveraging novel aggregate models of both DER parameters and sub-hourly regulation signals. In the hour-ahead model, we formulate a data-driven distributionally robust chance-constrained program to explicitly model the aforementioned uncertainties. This program can effectively control the quality of regulation service based on the aggregator's risk aversion. Furthermore, it learns the distributions of the uncertain parameters from empirical data so that it outperforms existing techniques (e.g., robust optimization or traditional chance-constrained programming) in both modeling accuracy and cost of robustness. Finally, we derive a conic safe approximation for it which can be efficiently solved by commercial solvers. Numerical experiments are conducted to validate the proposed method.

KeywordData-driven Distributionally Robust Chance-constraint Distributed Energy Resources Regulation Service Risk-averse
DOI10.1109/TSG.2018.2809046
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000466603800034
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Citation statistics
Cited Times [WOS]:16   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.Department of Electrical Engineering, Tsinghua University, Beijing, China
2.Department of Civil and Environmental Engineering, University of California at Berkeley, Berkeley, CA, USA
3.Tsinghua-Berkeley Shenzhen Institute, Shenzhen 518055, China
4.University of Macau, Macau 999078, China
Recommended Citation
GB/T 7714
Hongcai Zhang,Zechun Hu,Eric Munsing,et al. Data-Driven Chance-Constrained Regulation Capacity Offering for Distributed Energy Resources[J]. IEEE Transactions on Smart Grid,2018,10(3):2713-2725.
APA Hongcai Zhang,Zechun Hu,Eric Munsing,Scott J. Moura,&Yonghua Song.(2018).Data-Driven Chance-Constrained Regulation Capacity Offering for Distributed Energy Resources.IEEE Transactions on Smart Grid,10(3),2713-2725.
MLA Hongcai Zhang,et al."Data-Driven Chance-Constrained Regulation Capacity Offering for Distributed Energy Resources".IEEE Transactions on Smart Grid 10.3(2018):2713-2725.
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